@article{eprints2620,
          author = {Mohammed Abdelsamea and Giorgio Gnecco and Mohamed Medhat Gaber},
         journal = {Neurocomputing},
       publisher = {Elsevier},
           pages = {820 -- 835},
          number = {Part B},
           month = {February},
          volume = {149},
            year = {2015},
           title = {An efficient Self-Organizing Active Contour model for image segmentation },
        abstract = {Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, based on the evolution of an active contour. Among ACMs, supervised \{ACMs\} are able to exploit the information extracted from supervised examples to guide the contour evolution. However, their applicability is limited by the accuracy of the probability models they use. As a consequence, effectiveness and efficiency of supervised \{ACMs\} are among their main real challenges, especially when handling images containing regions characterized by intensity inhomogeneity. In this paper, to deal with such kinds of images, we propose a new supervised ACM, named Self-Organizing Active Contour (SOAC) model, which combines a variational level set method (a specific kind of ACM) with the weights of the neurons of two Self-Organizing Maps (SOMs). Its main contribution is the development of a new \{ACM\} energy functional optimized in such a way that the topological structure of the underlying image intensity distribution is preserved ? using the two \{SOMs\} ? in a parallel-processing and local way. The model has a supervised component since training pixels associated with different regions are assigned to different SOMs. Experimental results show the superior efficiency and effectiveness of \{SOAC\} versus several existing ACMs. },
             url = {http://eprints.imtlucca.it/2620/},
        keywords = {Region-based segmentation; Variational level set method;
    Active contours; Self-organizing neurons; Region-based prior knowledge}
}